查看文章

Surface defect inspection, detection, and classification in hollow cylindrical surfaces such as pipes and barrels have a significant impact on the structural integrity of various industrial products. Regular inspection and identification of the faults reduces the likelihood of faults’ aggravation, limits the damaging effects, and increases the product life. However, most of the defect detection algorithms for cylindrical surfaces rely heavily on handcrafted feature extraction limiting the ability to recognize the defects effectively. This research work proposes an image processing-based automatic defect detection and classification approach for cylindrical hollow surfaces. The proposed system uses a single shot multi-box detection (SSD) algorithm for localization and a customized lightweight deep convolutional neural network as a backbone network to classify defects generally found in industrial pipes and gun barrels. First, the …